|Title:||Status quo of big data analysis in small and medium size enterprises in Austria||Language:||English||Authors:||Schamp, Elmar||Qualification level:||Diploma||Advisor:||Lüthje, Christian||Issue Date:||2016||Number of Pages:||63||Qualification level:||Diploma||Abstract:||
Commentators around the world have emphasized the importance of data with statements like 'data is the oil of the next century' or 'data is the new oil' for the past decade. Has it come true? If data is so important, how important is Big Data? What is Big Data and what is the status of Big Data in Austria? This thesis is aimed to analyze the status quo of Big Data for small and medium sized enterprises in Austria. Therefore, the value chain of Big Data is used to define a basement for each single step and oppose this to actual findings that were extracted from surveys. The difference between traditional data analysis and Big Data Analytics are blurred and especially for SMEs irrelevant. It is not so much about the magnitude of data volume, velocity of processing and the variety of data (3Vs) but rather the value that can be generated out of data. New techniques like predictive analysis, machine learning and network analysis and technologies like analytical databases and parallel programming models are not yet in focus for most SMEs, although in terms of the best solution for a certain problem the enterprises will use the best model available. Use cases and success stories for Big Data in the manufacturing field like smart production lines and predicting service intervals are not sufficiently sound for SMEs to embark on the Big Data paradigm. The major challenges for Big Data initiatives are a lack of knowledge within the companies, a lack of skilled workforce, privacy concerns and data security issues. The latter problems should be addressed in a data strategy which should be developed upfront a Big Data initiative and has to be seen interconnected with the business strategy. To avoid missing business opportunities, SMEs should carefully analyze the potential impact that a deeper analysis of data could have on their customers and processes regardless of where the data will be acquired, how they will be stored or processed. For these technical issues, SMEs can easily find the right service facilitated by a Big Data platform service provider. Ventures that are not yet dependent on data like companies were on oil years ago certainly have further potential to improve productivity and service quality for their customers by starting Big Data initiatives.
|Keywords:||Big Data; Data Analytics; SME in Austria; Data Strategy; Big Data implementation hurdle||URI:||https://resolver.obvsg.at/urn:nbn:at:at-ubtuw:1-6085
|Library ID:||AC13306498||Organisation:||E017 - Continuing Education Center||Publication Type:||Thesis
|Appears in Collections:||Thesis|
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checked on May 16, 2021
checked on May 16, 2021
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